# Improve Statistical Methods for Profiling of Healthcare Providers

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $315,924

## Abstract

PROJECT SUMMARY
Healthcare provider profiling is of nationwide importance. In order to identify extreme (poor or excellent)
performance and to intervene as necessary, outcomes of patients associated with specific healthcare providers
are routinely monitored by both government and private payers. This monitoring can help patients make more
informed decisions, and can also aid consumers, stakeholders, and payers in identifying providers where
improvement may be needed, and even closing or fining those with extremely poor outcomes.
Our endeavor is motivated by the study of end-stage renal disease (ESRD), which represents 7.2% of the
entire Medicare budget and is of interest due to its heavy burden on patients, families, and the healthcare
system. Existing profiling approaches for analyzing large-scale ESRD registry data assume the risk adjustment
is perfect and the between-provider variation is entirely due to the quality of care, which is often invalid. As a
result, these methods disproportionately identify larger providers, although they need not be “extreme.'' To
address this problem, Aim 1 develops an individualized empirical null approach for profiling healthcare
providers to account for the unexplained between-provider variation due to imperfect risk adjustment.
The national dialysis data contains more than 3,000 comorbidities from over 2,000,000 patients who are
treated from more than 7,000 facilities. The goal is to select important comorbidity indexes for risk adjustment
of provider profiling. However, the use of large-scale databases introduces computational difficulties,
particularly when the event of interest is recurrent, and the numbers of sample size and the dimension of
parameters are large. Traditional methods that perform well for moderate sample sizes and low-dimensional
data do not scale to such massive data. Another challenging aspect of the national dialysis dataset is that
patient information is updated sequentially. How to integrate streaming recurrent event data adds another
level of difficulty. In view of these difficulties, Aim 2 proposes a nested divide-and-conquer-based boosting
procedure for high-dimensional variable selection with large-scale clustered recurrent event data. The
proposed procedure is further combined with a model updating procedure based on the time-dependent
Kullback-Leibler discrimination information to integrate streaming recurrent event data.
Finally, the COVID-19 pandemic has dramatically changed how healthcare care is delivered, and statisticians
have an important role to play in supporting providers and patients through this evolution. Aim 3 proposes a
latent illness-death model to account for temporal and geospatial variation of COVID prevalence in the provider
profiling. This analysis is needed to evaluate provider performance more accurately, to help physicians focus
on groups of patients with excess risk, and to aid providers in determining corrective actions to improve their
performance.
The...

## Key facts

- **NIH application ID:** 10817138
- **Project number:** 5R01DK129539-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Zhi Kevin He
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $315,924
- **Award type:** 5
- **Project period:** 2022-04-01 → 2027-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10817138

## Citation

> US National Institutes of Health, RePORTER application 10817138, Improve Statistical Methods for Profiling of Healthcare Providers (5R01DK129539-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10817138. Licensed CC0.

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